Video inspection systems, such as borescopes, have been widely used for capturing images or videos of difficult-to-reach locations by “snaking” image sensor(s) to these locations. Applications utilizing borescope inspections include automated video damage inspection, aerospace engine inspection, power turbine inspection, internal inspection of mechanical devices, medical endoscope inspection, and the like.
A variety of techniques for inspecting the images or videos provided by borescopes for determining defects therein have been proposed in the past. Most such techniques capture and display images or videos to human inspectors. Human inspectors then decide whether any defect within those images or videos exists. For engine inspection, when human inspectors look at many similar images of very similar blades of an engine stage, sometimes they miss defects because of the repetitive nature of the process or because of physical, visual, or mental fatigue experienced by the inspector. Missing a critical defect may lead to customer dissatisfaction, transportation of an expensive engine back to a service center, lost revenue, or lack of desired engine performance. Equally, there are adverse consequences for missed damage detection in other applications.
Accordingly, it would be beneficial if an improved technique for performing defect detection were developed that utilized multiple modes of information.